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Methods for Space-Time Analysis and Modeling: An Overview

Methods for Space-Time Analysis and Modeling: An Overview

Eric Delmelle, Changjoo Kim, Ningchuan Xiao, Wei Chen
Copyright: © 2013 |Volume: 4 |Issue: 4 |Pages: 18
ISSN: 1947-9654|EISSN: 1947-9662|EISBN13: 9781466635166|DOI: 10.4018/jagr.2013100101
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MLA

Delmelle, Eric, et al. "Methods for Space-Time Analysis and Modeling: An Overview." IJAGR vol.4, no.4 2013: pp.1-18. http://doi.org/10.4018/jagr.2013100101

APA

Delmelle, E., Kim, C., Xiao, N., & Chen, W. (2013). Methods for Space-Time Analysis and Modeling: An Overview. International Journal of Applied Geospatial Research (IJAGR), 4(4), 1-18. http://doi.org/10.4018/jagr.2013100101

Chicago

Delmelle, Eric, et al. "Methods for Space-Time Analysis and Modeling: An Overview," International Journal of Applied Geospatial Research (IJAGR) 4, no.4: 1-18. http://doi.org/10.4018/jagr.2013100101

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Abstract

With increasing availability of spatio-temporal data and the democratization of Geographical Information Systems (GIS), there has been a demand for novel statistical and visualization techniques which can explicitly integrate space and time. The paper discusses the nature of spatio-temporal data, the integration of time within GIS and the flourishing availability of spatial and temporal-explicit data over the Internet. The paper attempts to answer the fundamental question on how these large datasets can be analyzed in space and time to reveal critical patterns. The authors further elaborate on how spatial autocorrelation techniques are extended to deal with time, for point, linear, and areal features, and the impact of parameter selection, such as critical distance and time threshold to build adjacency matrices. The authors also discuss issues of space-time modeling for optimization problems.

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